- Supervised and unsupervised learning
- Time series modeling
- Linear and logistic regression
- Kernel SVM
- KMeans clustering
- Naive Bayes
- Decision tree
- Random forest classifiers
- Boosting and Bagging techniques
- Deep Learning fundamentals

- Analytics Managers
- Business Analysts
- Information Architects
- Developers

### Machine Learning

#### Lesson 01: Course Introduction

09:19##### 1.01 Course Introduction

06:08##### 1.02 Demo: Jupyter Lab Walk - Through

03:11

#### Lesson 02: Introduction to Machine Learning

08:40##### 2.01 Learning Objectives

00:42##### 2.02 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part A

02:46##### 2.03 Relationship between Artificial Intelligence, Machine Learning, and Data Science: Part B

01:23##### 2.04 Definition and Features of Machine Learning

01:30##### 2.05 Machine Learning Approaches

01:46##### 2.06 Key Takeaways

00:33

#### Lesson 03: Supervised Learning Regression and Classification

02:10:59##### 3.01 Learning Objectives

00:46##### 3.02 Supervised Learning

02:18##### 3.03 Supervised Learning: Real Life Scenario

00:55##### 3.04 Understanding the Algorithm

00:54##### 3.05 Supervised Learning Flow

01:51##### 3.06 Types of Supervised Learning: Part A

01:57##### 3.07 Types of Supervised Learning: Part B

02:05##### 3.08 Types of Classification Algorithms

01:03##### 3.09 Types of Regression Algorithms: Part A

03:23##### 3.10 Regression Use Case

00:36##### 3.11 Accuracy Metrics

01:24##### 3.12 Cost Function

01:49##### 3.13 Evaluating Coefficients

00:55##### 3.14 Demo: Linear Regression

13:48##### 3.15 Challenges in Prediction

01:47##### 3.16 Types of Regression Algorithms: Part B

02:40##### 3.17 Demo: Bigmart

37:29##### 3.18 Logistic Regression: Part A

02:01##### 3.19 Logistic Regression: Part B

01:41##### 3.20 Sigmoid Probability

02:07##### 3.21 Accuracy Matrix

01:28##### 3.22 Demo: Survival of Titanic Passengers

13:17##### 3.23 Overview of Classification

02:03##### 3.24 Classification: A Supervised Learning Algorithm

00:52##### 3.25 Use Cases

02:34##### 3.26 Classification Algorithms

00:17##### 3.27 Performance Measures: Confusion Matrix

02:21##### 3.28 Performance Measures: Cost Matrix

02:07##### 3.29 Naive Bayes Classifier

01:16##### 3.30 Steps to Calculate Posterior Probability: Part A

01:41##### 3.31 Steps to Calculate Posterior Probability: Part B

02:22##### 3.32 Support Vector Machines: Linear Separability

01:05##### 3.33 Support Vector Machines: Classification Margin

02:06##### 3.34 Linear SVM: Mathematical Representation

02:05##### 3.35 Non linear SVMs

01:07##### 3.36 The Kernel Trick

01:19##### 3.37 Demo: Voice Classification

10:42##### 3.38 Key Takeaways

00:48

#### Lesson 04: Decision Trees and Random Forest

18:09##### 4.01 Learning Objectives

00:37##### 4.02 Decision Tree: Classifier

02:17##### 4.03 Decision Tree: Examples

01:44##### 4.04 Decision Tree: Formation

00:46##### 4.05 Choosing the Classifier

02:56##### 4.06 Overfitting of Decision Trees

01:01##### 4.07 Random Forest Classifier Bagging and Bootstrapping

02:19##### 4.08 Decision Tree and Random Forest Classifier

01:07##### 4.09 Demo: Horse Survival

04:57##### 4.10 Key Takeaways

00:25

#### Lesson 05: Unsupervised Learning

32:41##### 5.01 Learning Objectives

00:36##### 5.02 Overview

01:47##### 5.03 Example and Applications of Unsupervised Learning

02:17##### 5.04 Clustering

01:46##### 5.05 Hierarchical Clustering

02:30##### 5.06 Hierarchical Clustering: Example

02:02##### 5.07 Demo: Clustering Animals

05:40##### 5.08 K-means Clustering

03:54##### 5.09 Optimal Number of Clusters

03:27##### 5.10 Demo: Cluster Based Incentivization

08:18##### 5.11 Key Takeaways

00:24

#### Lesson 06: Time Series Modelling

38:57##### 6.01 Learning Objectives

00:24##### 6.02 Overview of Time Series Modeling

02:16##### 6.03 Time Series Pattern Types: Part A

02:16##### 6.04 Time Series Pattern Types: Part B

01:19##### 6.05 White Noise

01:06##### 6.06 Stationarity

02:13##### 6.07 Removal of Non Stationarity

02:13##### 6.08 Demo: Air Passengers I

14:26##### 6.09 Time Series Models: Part A

02:14##### 6.10 Time Series Models: Part B

01:28##### 6.11 Time Series Models: Part C

01:51##### 6.12 Steps in Time Series Forecasting

00:37##### 6.13 Demo: Air Passengers II

06:14##### 6.14 Key Takeaways

00:20

#### Lesson 07: Ensemble Learning

39:35##### 7.01 Learning Objectives

00:24##### 7.02 Overview

02:41##### 7.03 Ensemble Learning Methods: Part A

02:49##### 7.04 Ensemble Learning Methods: Part B

04:09##### 7.05 Working of AdaBoost

01:43##### 7.06 AdaBoost Algorithm and Flowchart

02:28##### 7.07 Gradient Boosting

04:37##### 7.08 XGBoost

02:23##### 7.09 XGBoost Parameters: Part A

03:15##### 7.10 XGBoost Parameters: Part B

02:30##### 7.11 Demo: Pima Indians Diabetes

03:11##### 7.12 Model Selection

02:55##### 7.13 Common Splitting Strategies

01:45##### 7.14 Demo: Cross Validation

04:18##### 7.15 Key Takeaways

00:27

#### Lesson 08: Recommender Systems

26:11##### 8.01 Learning Objectives

00:27##### 8.02 Introduction

02:16##### 8.03 Purposes of Recommender Systems

00:45##### 8.04 Paradigms of Recommender Systems

02:45##### 8.05 Collaborative Filtering: Part A

02:14##### 8.06 Collaborative Filtering: Part B

01:58##### 8.07 Association Rule: Mining

01:47##### 8.08 Association Rule: Mining Market Basket Analysis

01:42##### 8.09 Association Rule: Generation Apriori Algorithm

00:53##### 8.10 Apriori Algorithm Example: Part A

02:13##### 8.11 Apriori Algorithm Example: Part B

01:17##### 8.12 Apriori Algorithm: Rule Selection

02:52##### 8.13 Demo: User Movie Recommendation Model

04:12##### 8.14 Key Takeaways

00:50

#### Lesson 09: Level Up Sessions

10:31##### Session 01

05:22##### Session 02

05:09

#### Practice Project

##### California Housing Price Prediction

##### Phishing Detector with LR

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Expected machine learning market growth by 2022

In the adoption of machine learning in organizations

- Average Salary
### $83K - $154K Per Annum

Hiring Companies - Average Salary
### $78K - $150K Per Annum

Hiring Companies

### What are the prerequisites to learn the Machine Learning basics program?

Prior knowledge of basic mathematics, statistics, and Python programming is beneficial to take this machine learning basics course.

### How do beginners learn Machine Learning basics?

Beginners often rely on free online tutorials or to learn the fundamentals of Machine Learning. For a reliable start in this field, Simplilearnâ€™sÂ free Machine Learning for beginnersÂ course is an excellent option and then you can move on to our AI and ML Course.

### How long does it take to learn Machine Learning?

The time required to learn machine learning varies for every learner depending on their educational background and prior exposure in the field. The 7 hours of online content covered in this course will surely help you get the ML basics right in a short amount of time.

### What should I learn first in the Machine Learning basics program?

Professionals who wish to start with Machine Learning first get a complete overview of Artificial Intelligence, its applications and how various industries are using it. Then they learn about Machine Learning, its approaches and techniques.

### Is the Machine Learning foundations program easy to learn?

Simplilearn curates all of its courses as per the learnersâ€™ needs. Even if you donâ€™t have any prior idea of Machine Learning, it will be easy for you to follow the video lessons covered in this Machine Learning fundamentals program.

### What are the basics in a Machine Learning foundations training program?

Simplilearnâ€™s Machine Learning free online course starts with the basics like how Machine Learning is related to artificial intelligence, common terminologies in this field, and types of Machine Learning - supervised, unsupervised, and reinforced.

- Disclaimer
- PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.
- *According to Simplilearn survey conducted and subject to terms & conditions with Ernst & Young LLP (EY) as Process Advisors